The association between California Verbal Learning Test performance and fibre impairment in multiple sclerosis: evidence from diffusion tensor imaging
Bibliographic record
Abstract
The California Verbal Learning Test (CVLT) is recognized as a standard clinical tool for assessing episodic memory difficulties in multiple sclerosis (MS), but its neural correlates have not yet been examined in detail in this patient population. We combined neuropsychological examination and diffusion tensor imaging (DTI) analysis in a group of MS patients (N = 50) and demographically matched healthy participants (N = 20). We investigated the degree of impairment of the uncinate fascicle (UF), the superior longitudinal fascicle (SLF), the fornix (FX) and the cingulum (CG). The patients were impaired on all CVLT parameters and the DTI parameters correlated moderately with disease-related variables. Regression analyses in the complete study sample showed that CVLT learning scores correlated with impairment of the right UF. This association reached marginal significance in the patient sample. In contrast to other studies claiming retrieval deficits, our results suggest that encoding and consolidation deficits may play a major role in verbal memory impairments in MS. The findings also provide evidence for an association between degree of myelination of prefrontal fibre pathways and encoding efficiency. Finally, DTI-derived measurements appear to reflect disease progression in MS. The results are discussed in light of functional MRI studies investigating compensatory brain activity during cognitive processing in MS.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".